对先进推理与基线大语言模型进行比较的研究,用于口腔和面病理学中的组织病理学诊断
Viet Anh Nguyen1, Van Hung Nguyen2, Thi Quynh Trang Vuong3
1Faculty of Dentistry, Phenikaa University, Hanoi, Viet Nam.
PloS one
|December 31, 2025
概括
与早期版本相比,新的推理增强大语言模型 (LLM) 在数字病理学中显示出更好的诊断准确性. 然而,临床应用需要优化速度和一致性.
科学领域:
- 数字病理学数字病理学
- 人工智能在医学中的应用
- 组织病理学 诊断 诊断 诊断
背景情况:
- 大型语言模型 (LLM) 正在被探索为数字病理学中的诊断辅助工具.
- 先进推理增强的LLM架构与基线模型的性能尚未得到充分证实.
研究的目的:
- 为了比较推理增强的LLM (o3) 与口腔和面 (OMF) 组织病理学的基线LLM (GPT-4o) 的诊断性能.
- 评估模型的诊断准确性,描述细节,响应时间和可重复性.
主要方法:
- 使用教科书中的459个OMF组织病理病例的数据集.
- 两个LLM (o3和GPT-4o) 在零射击模式下查询诊断和配套功能.
- 模型的性能与专家病理学家确定的基本事实进行了比较.
主要成果:
- 3模型的准确度 (31.6%) 比GPT-4o (18.7%) (P < 0.001) 高得多.
- o3为正确诊断的病例提供了更详细的描述.
- 与GPT-4o相比,o3表现出更长的响应时间和更低的可复制性.
结论:
- 推理增强的LLM在复杂的组织病理学中表现出更好的诊断性能和解释深度.
- 需要进一步优化以满足临床要求的速度和一致性.
- 目前,LLM作为辅助工具,专家病理学家保留最终诊断责任.
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